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Task Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation

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Task Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation Research Articles: Behavioral/Cognitive Task related sensorimotor adjustments increase the sensory range in electrolocation https://doi.org/10.1523/JNEUROSCI.1024-19.2019 Cite as: J. Neurosci 2019; 10.1523/JNEUROSCI.1024-19.2019 Received: 14 May 2019 Revised: 9 November 2019 Accepted: 18 November 2019 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.jneurosci.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2019 the authors Title: Task related sensorimotor adjustments increase the sensory range in electrolocation Abbreviated title: Sensorimotor learning improves the sensory range Authors: Federico Pedraja1, Volker Hofmann1,2, Julie Goulet1, Jacob Engelmann1* Affiliations: 1 Bielefeld University, Faculty of Biology/CITEC, AG Active Sensing, Postfach 100131, D-33501 Biele- feld, GERMANY 2 McGill University, Department of Physiology, 3655 Promenade Sir William Osler, Montreal, QC, H3G 1Y6 CANADA * Corresponding author: Jacob Engelmann AG Active Sensing Bielefeld University D-33501 Bielefeld, Germany Tel +49-521-106-4641 [email protected] Number of pages: 38 Number of figures: 9 Number of words: abstract (163 words), introduction (556 words), discussion (1609 words). Competing interests: Authors of this manuscript do not have any financial or non-financial compet- ing interests. Acknowledgments: This work was supported by the Cluster of Excellence Cognitive Interaction Tech- nology ‘CITEC’ (EXC 277) and the DFG (EN 826/5-1). ABSTRACT 1 Perception and motor control traditionally are studied separately. However, motor activity can serve 2 as a scaffold to shape the sensory flow. This tight link between motor actions and sensing is particu- 3 larly evident in active sensory systems. Here, we investigate how the weakly electric mormyrid fish 4 Gnathonemus petersii of undetermined sex structure their sensing and motor behavior while learn- 5 ing a perceptual task. We find systematic adjustments of the motor behavior that correlate with an 6 increased performance. Using a model to compute the electrosensory input, we show that these 7 behavioural adjustments improve the sensory input. As we find low neuronal detection thresholds at 8 the level of medullary electrosensory neurons, it seems that the behavior-driven improvements of 9 the sensory input are highly suitable to overcome the sensory limitations, thereby increasing the 10 sensory range. Our results show that motor control is an active component of sensory learning, 11 demonstrating that a detailed understanding of contribution of motor actions to sensing is needed to 12 understand even seemingly simple behaviors. 1 13 SIGNIFICANCE STATEMENT 14 Motor-guided sensation and perception are intertwined, with motor behavior serving as a scaffold to 15 shape the sensory input. We characterized how the weakly electric mormyrid fish G. petersii, as it 16 learns a perceptual task, restructures its sensorimotor behavior. We find that systematic adjustments 17 of the motor behavior correlate with increased performance and a shift of the animal’s sensory at- 18 tention. Analyzing the afferent electrosensory input shows that a significant gain in information re- 19 sults from these sensorimotor adjustments. Our results show that motor control can be an active 20 component of sensory learning. Researching the sensory corollaries of motor control thus can be 21 crucial to understand sensory sensation and perception under naturalistic conditions. 2 INTRODUCTION 22 Exploratory behavior is a crucial substrate for learning (Loewenstein, 1994). As the animals’ move- 23 ments influence the sensory input, re-organizing the motor patterns with respect to recent experi- 24 ence may contribute to learning thereby improving behavior. Analyzing these modifications can thus 25 reveal how motor action contributes to learning (Wolpert and Landy, 2012; O’Hora et al., 2013) and 26 aid decision making through action selection (Charlesworth et al., 2011; O’Hora et al., 2013; 27 Zgonnikov et al., 2017). 28 While the variability of motor behavior may facilitate motor learning by widening the search space 29 from which behaviors are instantiated (Brainard and Doupe, 2013; Wu et al., 2014), the same varia- 30 bility can set bounds on the task-optimization of motor control (van Beers et al., 2002). This is partic- 31 ularly evident in active sensory systems, where the sensory input directly depends on the motor out- 32 put. Here the strong sensorimotor dependencies may be exploited by an animal to adjust motor be- 33 havior in order to not only improve the motor but also the sensing efficiency (Friston, 2010; Little and 34 Sommer, 2013; Gordon et al., 2014). 35 36 We here investigated how sensorimotor behavior changes while Gnathonemus petersii, a pulse type 37 weakly electric fish, learned a detection task. During active electrolocation these fish obtain sensory 38 information through brief discharges of a specialized electric organ in their tail (electric organ dis- 39 charge, EOD). The discharge rate is under top-down control and changes in a context-dependent 40 manner (Post and von der Emde, 1999; Caputi et al., 2003). Each emitted EOD creates a 3- 41 dimensional electric field around the fish which is perturbed by nearby objects (Lissmann and 42 Machin, 1958). Also motion of the animal can perturb the electric field (e.g., tail movement (Sawtell 43 et al., 2006)), both of which are perceived by electroreceptors in the skin of the fish. To discriminate 44 between the predictable (re-afferent) and unpredictable (ex-afferent) components of the sensory 45 input, weakly electric fish are known to rely on a sophisticated neuronal circuitry (Sawtell et al., 46 2005; Bell et al., 2008) which enables them to analyze their nearby environment. 47 Not all (re-afferent) sensory consequences of behavior must be unfavorable however: Similar to oth- 48 er organisms (Poteser and Kral, 1995; Kern et al., 2001), weakly electric fish exhibit a variety of stere- 49 otyped behaviors (Toerring and Belbenoit, 1979; Toerring and Moller, 1984; Nelson and Maciver, 50 1999; Hofmann et al., 2014). Recent studies have revealed that behaviorally relevant sensory infor- 51 mation can emerge from such strongly patterned sensorimotor behaviors, i.e. weakly electric fish 52 actively exploit these sensorimotor dependencies (Hofmann et al., 2017; Pedraja et al., 2018). 3 53 The ability to control the timing of sensory sampling while at the same time being able to shape the 54 properties of the sensory input through their motor behavior makes weakly electric fish particularly 55 suitable to study how changes in exploratory behaviors can guide sensory-driven learning efficiently. 56 We here focussed on a reinforced object detection task and found that performance was progres- 57 sively enhanced by consistent changes of the motor patterns. These changes resulted in an increased 58 sensory range. Our results add further support to the idea that weakly electric fish actively improve 59 sensing capabilities by selecting purposeful components from their motor repertoire and focus their 60 electric attention in a goal-directed manner. Such behavioral control of the sensory input might con- 61 tribute to improving neuronal stimulus detection and encoding, as we found neuronal performance 62 to be relatively poor at the level of the medulla. 63 4 64 MATERIAL AND METHODS 65 Animals. Wild-caught Gnathonemus petersii of either sex were obtained from a commercial fish 66 dealer (Aquarium Glaser, Rodgau, Germany) and housed in communal 400L aquaria. The water tem- 67 perature in these aquaria and the set-up was 25 ± 1 °C at a conductivity of 100 ± 5 μS cm−1 and a 68 12L:12D photoperiod. Fish were fed with bloodworms. All procedures for animal maintenance and 69 preparations comply with the current animal protection law of the Federal Republic of Germany and 70 have been approved by the local authorities (Landesamt für Natur, Umwelt und Verbraucherschutz 71 Nordrhein-Westfalen: 87–51- 04.2010.A202). 72 Behavior 73 Training setup. Five G. petersii (length of 11 ± 1 cm) housed in separate experimental tanks and fed 74 with bloodworms to satiation three times per week before the beginning of the behavioral experi- 75 ment. The experimental tanks (120 · 50 · 50 cm) were divided in a living area (60 · 50 cm; 30 cm, wa- 76 ter level) that was separated by a plastic gate from the experimental area (60 · 50 cm; 10 cm, water 77 level). The floor in the experimental area was 20 cm above the floor of the living area, which con- 78 fined the movements of the animal in the experimental area into two dimensions. A plastic plate 79 divided the proximal end (20 cm) of the experimental arena in two compartments. Perpendicular to 80 this plate a 1 cm wide plastic strip marked the entry to the compartments on the floor. A metal cube 81 (2 · 2 · 2 cm) was placed on the floor at the decision line, centered in front of the cued compartment 82 where it served as a cue to the rewarded compartment (S+). Experiments were performed in dark- 83 ness (< 0.1 lux measured above the water level) and videotaped from the top (60 fps; AVT Marlin F- 84 131 & F-033) using IR-illumination (880 nm) from below. This wavelength is beyond the perceptual 85 range of this species (Ciali et al., 1997). EODs were recorded differentially (custom-built electrode 86 array, 0.6 – 40 kHz band pass) and stored as events (PC audio card, 12 bit, 10 kHz) alongside acquired 87 videos. 88 Experimental design, video tracking and statistical analysis. Animals first learned to swim through 89 the opened gate to the proximal end of the arena to receive food. Once fish did this reliably training 90 commenced, and videos of each trial were acquired.
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